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We present SpecAugment, a simple data augmentation method for speech recognition.
J. Godfrey, E. Holliman, and J. McDaniel, “SWITCHBOARD: telephone speech corpus for research and development,” in
1992
Earlier work this paper cites.
C. Cieri, D. Miller, and K. Walker, “The fisher corpus: a resource for the next generations of speech-to-text,” in
2004
Earlier work this paper cites.
A. Graves, “Practical Variational Inference for Neural Networks,” in
2011
Earlier work this paper cites.
D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz, J. Silovsky, G. Stemmer, and K. Vesely, “The Kaldi Speech Recognition Toolkit,” in
2011
Earlier work this paper cites.
G. Hinton, L. Deng, D. Yu, G. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, B. Kingsbury
2012
Earlier work this paper cites.
G. Dahl, D. Yu, L. Deng, and A. Acero, “Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition,”
2012
Earlier work this paper cites.
M. Schuster and K. Nakajima, “Japanese and korean voice search,” in
2012
Earlier work this paper cites.
T. Sainath, A. rahman Mohamed, B. Kingsbury, and B. Ramabhadran, “Deep Convolutional Neural Networks for LVCSR,” in
2013
Earlier work this paper cites.
A. Graves, A. rahman Mohamed, and G. Hinton, “Speech Recognition with Deep Recurrent Neural Networks,” in
2013
Earlier work this paper cites.
N. Kanda, R. Takeda, and Y. Obuchi, “Elastic spectral distortion for low resource speech recognition with deep neural networks,” in
2013
Earlier work this paper cites.
N. Jaitly and G. Hinton, “Vocal Tract Length Perturbation (VTLP) improves speech recognition,” in
2013
Earlier work this paper cites.
K. Vesely, A. Ghoshal, L. Burger, and D. Povey, “Sequence-discriminative training of deep neural networks,” in
2013
Earlier work this paper cites.
A. Graves and N. Jaitly, “Towards End-to-End Speech Recognition with Recurrent Neural Networks,” in
2014
Earlier work this paper cites.
A. Ragni, K. M. Knill, S. P. Rath, and M. J. F. Gales, “Data augmentation for low resource languages,” in
2014
Earlier work this paper cites.
A. Hannun, C. Case, J. Casper, B. Catanzaro, G. Diamos, E. Elsen, R. Prenger, S. Satheesh, S. Sengupta, A. Coates, and A. Ng, “Deep Speech: Scaling up end-to-end speech recognition,” in
2014
Earlier work this paper cites.
T. Ko, V. Peddinti, D. Povey, and S. Khudanpur, “Audio Augmentation for Speech Recognition,” in
2015
Earlier work this paper cites.
R. Prabhavalkar, R. Alvarez, C. Parada, P. Nakkiran, and T. N. Sainath, “Automatic gain control and multi-style training for robust small-footprint keyword spotting with deep neural networks,” in
2015
Earlier work this paper cites.
V. Panayotov, G. Chen, D. Povey, and S. Khudanpur, “Librispeech: An ASR corpus based on public domain audio books,” in
2015
Cited alongside, same era.
Ç. Gülçehre, O. Firat, K. Xu, K. Cho, L. Barrault, H. Lin, F. Bougares, H. Schwenk, and Y. Bengio, “On using monolingual corpora in neural machine translation,” in
2015
Cited alongside, same era.
W. Chan, N. Jaitly, Q. V. Le, and O. Vinyals, “Listen, Attend and Spell: A Neural Network for Large Vocabulary Conversational Speech Recognition,” in
2016
Cited alongside, same era.
D. Bahdanau, J. Chorowski, D. Serdyuk, P. Brakel, and Y. Bengio, “End-to-End Attention-based Large Vocabulary Speech Recognition,” in
2016
Cited alongside, same era.
S. H. Mallidi and H. Hermansky, “Novel neural network based fusion for Multistream ASR,” in
2016
Cited alongside, same era.
C.-C. Chiu, T. N. Sainath, Y. Wu, R. Prabhavalkar, P. Nguyen, Z. Chen, A. Kannan, R. J. Weiss, K. Rao, E. Gonina, N. Jaitly, B. Li, J. Chorowski, and M. Bacchiani, “State-of-the-art Speech Recognition With Sequence-to-Sequence Models,” in
2018
Later among the works it cites.
A. Raju, S. Panchapagesan, X. Liu, A. Mandal, and N. Strom, “Data Augmentation for Robust Keyword Spotting under Playback Interference,” in
2018
Later among the works it cites.
A. Zeyer, K. Irie, R. Schlüter, and H. Ney, “Improved training of end-to-end attention models for speech recognition,” in
2018
Later among the works it cites.
X. Yang, J. Li, and X. Zhou, “A novel pyramidal-FSMN architecture with lattice-free MMI for speech recognition,” in
2018
Later among the works it cites.
Y. Zhou, C. Xiong, and R. Socher, “Improving End-to-End Speech Recognition with Policy Learning,” in
2018
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C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in
2016
Cited alongside, same era.
D. Povey, V. Peddinti, D. Galvez, P. Ghahrmani, V. Manohar, X. Na, Y. Wang, and S. Khudanpur, “Purely sequence-trained neural networks for ASR based on lattice-free MMI,” in
2016
Cited alongside, same era.
R. Collobert, C. Puhrsch, and G. Synnaeve, “Wav2Letter: an End-to-End ConvNet-based Speech Recognition System,” in
2016
Cited alongside, same era.
L. Lu, X. Zhang, and S. Renals, “On training the recurrent neural network encoder-decoder for large vocabulary end-to-end speech recognition,” in
2016
Cited alongside, same era.
C. Kim, A. Misra, K. Chin, T. Hughes, A. Narayanan, T. Sainath, and M. Bacchiani, “Generation of large-scale simulated utterances in virtual rooms to train deep-neural networks for far-field speech recognition in Google Home,” in
2017
Cited alongside, same era.
T. DeVries and G. Taylor, “Improved Regularization of Convolutional Neural Networks with Cutout,” in
2017
Cited alongside, same era.
J. Chorowski and N. Jaitly, “Towards better decoding and language model integration in sequence to sequence models,” in
2017
Cited alongside, same era.
Later among the works it cites.
N. Zeghidour, Q. Xu, V. Liptchinsky, N. Usunier, G. Synnaeve, and R. Collobert, “Fully Convolutional Speech Recognition,” in
2018
Later among the works it cites.
A. Zeyer, A. Merboldt, R. Schlüter, and H. Ney, “A comprehensive analysis on attention models,” in
2018
Later among the works it cites.
H. Hadian, H. Sameti, D. Povey, and S. Khudanpur, “End-to-end speech recognition using lattice-free MMI,” in
2018
Later among the works it cites.
K. Audhkhasi, B. Ramabhadran, G. Saon, M. Picheny, and D. Nahamoo, “Direct Acoustics-to-Word Models for English Conversational Speech Recognition,” in
2018
Later among the works it cites.
K. Audhkhasi, B. Kingsbury, B. Ramabhadran, G. Saon, and M. Picheny, “Building competitive direct acoustics-to-word models for english conversational speech recognition,” in
2018
Later among the works it cites.
C. Weng, J. Cui, G. Wang, J. Wang, C. Yu, D. Su, and D. Yu, “Improving Attention Based Sequence-to-Sequence Models for End-to-End English Conversational Speech Recognition,” in
2018
Later among the works it cites.
L. Tóth, G. Kovács, and D. Van Compernolle, “A perceptually inspired data augmentation method for noise robust cnn acoustic models,” in
2018
Later among the works it cites.
E. D. Cubuk, B. Zoph, D. Mané, V. Vasudevan, and Q. V. Le, “Autoaugment: Learning augmentation policies from data,” in
2019
Closest in time.
K. Irie, R. Prabhavalkar, A. Kannan, A. Bruguier, D. Rybach, and P. Nguyen, “Model Unit Exploration for Sequence-to-Sequence Speech Recognition,” in
2019
Closest in time.
J. Li, V. Lavrukhin, B. Ginsburg, R. Leary, O. Kuchaiev, J. M. Cohen, H. Nguyen, and R. T. Gadde, “Jasper: An End-to-End Convolutional Neural Acoustic Model,” in
2019
Closest in time.
S. Sabour, W. Chan, and M. Norouzi, “Optimal Completion Distillation for Sequence Learning,” in
2019
Closest in time.